


"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def 				_snake_case      (   __snake_case							:      Tuple   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		os.path.join(args.tf_model_dir						, """parameters.json"""   )
   _lowerCamelCase	:					Tuple   =		json.loads(open(__snake_case   ).read()   )
   if not params:
      raise ValueError(
          F'It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'   )
   if not args.output.endswith(""".pt"""   ):
      _lowerCamelCase	:					str   =		args.output + """.pt"""
   _lowerCamelCase	:					int   =		OrderedDict()
   with tf.device("""/CPU:0"""   ):
      _lowerCamelCase	:					str   =		tf.train.load_checkpoint(args.tf_model_dir   )
      _lowerCamelCase	:					List[str]   =		reader.get_variable_to_shape_map()
      for key_name in shapes.keys():
         _lowerCamelCase	:					Union[str, Any]   =		reader.get_tensor(__snake_case   ).astype(np.floataa   )
         if key_name.endswith("""/adam_m"""   ) or key_name.endswith("""/adam_v"""   ):
            continue
         if key_name.startswith("""pasts/"""   ):
            if key_name.startswith("""pasts/mlp"""   ):
               _lowerCamelCase	:					Tuple   =		int(key_name[9]   )
            elif key_name.startswith("""pasts/out"""   ):
               _lowerCamelCase	:					List[str]   =		8
            _lowerCamelCase	:					str   =		"""model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
            _lowerCamelCase	:					int   =		vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
            _lowerCamelCase	:					List[Any]   =		torch.tensor(__snake_case   )
         elif key_name.startswith("""model/moe"""   ):
            _lowerCamelCase	:					Tuple   =		int(key_name[9:].split("""/"""   )[0]   )
            if key_name.endswith("""/switch_gating/kernel"""   ):
               _lowerCamelCase	:					Optional[Any]   =		"""model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
               _lowerCamelCase	:					Optional[int]   =		vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
               _lowerCamelCase	:					List[str]   =		torch.tensor(__snake_case   )
            elif key_name.endswith("""/softmlp/kernel"""   ):
               _lowerCamelCase	:					Optional[Any]   =		"""model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
               _lowerCamelCase	:					Any   =		vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
               _lowerCamelCase	:					str   =		torch.tensor(__snake_case   )
            elif key_name.endswith("""/wo/kernel"""   ) or key_name.endswith("""/wi/kernel"""   ):
               _lowerCamelCase	:					List[str]   =		key_name[-9:-7]
               for i in range(16   ):
                  _lowerCamelCase	:					Any   =		"""model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
                  _lowerCamelCase	:					Optional[int]   =		(
                      vnp[i].transpose([1, 0]   ).copy()
                  )  # In Mesh-Tensorflow, it is one array, so it is divided
                  _lowerCamelCase	:					List[str]   =		torch.tensor(__snake_case   )
         elif key_name.startswith("""model/mlp"""   ):
            _lowerCamelCase	:					int   =		int(key_name[9:].split("""/"""   )[0]   )
            if key_name.endswith("""/p1/kernel"""   ):
               _lowerCamelCase	:					Dict   =		"""model.blocks.%d.feed_forward.mlp.wi.weight""" % player
               _lowerCamelCase	:					Optional[int]   =		vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
               _lowerCamelCase	:					Dict   =		torch.tensor(__snake_case   )
            elif key_name.endswith("""/p1/bias"""   ):
               _lowerCamelCase	:					Tuple   =		"""model.blocks.%d.feed_forward.mlp.wi.bias""" % player
               _lowerCamelCase	:					Tuple   =		vnp.copy()  # same because it is one dimensional
               _lowerCamelCase	:					List[Any]   =		torch.tensor(__snake_case   )
            elif key_name.endswith("""/p2/kernel"""   ):
               _lowerCamelCase	:					List[str]   =		"""model.blocks.%d.feed_forward.mlp.wo.weight""" % player
               _lowerCamelCase	:					Tuple   =		vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
               _lowerCamelCase	:					List[Any]   =		torch.tensor(__snake_case   )
            elif key_name.endswith("""/p2/bias"""   ):
               _lowerCamelCase	:					str   =		"""model.blocks.%d.feed_forward.mlp.wo.bias""" % player
               _lowerCamelCase	:					Dict   =		vnp.copy()  # same because it is one dimensional
               _lowerCamelCase	:					List[str]   =		torch.tensor(__snake_case   )
         elif key_name.startswith("""model/ln"""   ):
            _lowerCamelCase	:					List[Any]   =		int(key_name[8:].split("""/"""   )[0]   )
            if key_name.endswith("""/b"""   ):
               _lowerCamelCase	:					Any   =		"""model.blocks.%d.feed_forward.norm.bias""" % player
               _lowerCamelCase	:					Optional[Any]   =		vnp.copy()  # same because it is one dimensional
               _lowerCamelCase	:					Optional[Any]   =		torch.tensor(__snake_case   )
            elif key_name.endswith("""/g"""   ):
               _lowerCamelCase	:					List[str]   =		"""model.blocks.%d.feed_forward.norm.weight""" % player
               _lowerCamelCase	:					int   =		vnp.copy()  # same because it is one dimensional
               _lowerCamelCase	:					int   =		torch.tensor(__snake_case   )
         elif key_name.startswith("""model/att"""   ):
            _lowerCamelCase	:					List[Any]   =		int(key_name[9:].split("""/"""   )[0]   )
            if key_name.endswith("""/qkv/kernel"""   ):
               _lowerCamelCase	:					Tuple   =		vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
               _lowerCamelCase	:					List[Any]   =		state[:, 0, :, :]
               _lowerCamelCase	:					Tuple   =		state[:, 1, :, :]
               _lowerCamelCase	:					List[Any]   =		state[:, 2, :, :]
               _lowerCamelCase	:					int   =		(
                   state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]   )
                   .transpose([1, 0]   )
                   .copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               _lowerCamelCase	:					Any   =		(
                   state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]   )
                   .transpose([1, 0]   )
                   .copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               _lowerCamelCase	:					Union[str, Any]   =		(
                   state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]   )
                   .transpose([1, 0]   )
                   .copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               _lowerCamelCase	:					Any   =		"""model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
               _lowerCamelCase	:					Dict   =		torch.tensor(__snake_case   )
               _lowerCamelCase	:					List[str]   =		"""model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
               _lowerCamelCase	:					Any   =		torch.tensor(__snake_case   )
               _lowerCamelCase	:					str   =		"""model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
               _lowerCamelCase	:					Union[str, Any]   =		torch.tensor(__snake_case   )
            elif key_name.endswith("""/o/kernel"""   ):
               _lowerCamelCase	:					Union[str, Any]   =		"""model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
               _lowerCamelCase	:					Any   =		(
                   vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]   ).transpose([1, 0]   ).copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               _lowerCamelCase	:					Optional[int]   =		torch.tensor(__snake_case   )
         elif key_name.startswith("""model/an"""   ):
            _lowerCamelCase	:					Optional[int]   =		int(key_name[8:].split("""/"""   )[0]   )
            if key_name.endswith("""/b"""   ):
               _lowerCamelCase	:					Union[str, Any]   =		"""model.blocks.%d.self_attn.norm.bias""" % player
               _lowerCamelCase	:					Dict   =		vnp.copy()  # same because it is one dimensional
               _lowerCamelCase	:					Tuple   =		torch.tensor(__snake_case   )
            elif key_name.endswith("""/g"""   ):
               _lowerCamelCase	:					str   =		"""model.blocks.%d.self_attn.norm.weight""" % player
               _lowerCamelCase	:					Optional[int]   =		vnp.copy()  # same because it is one dimensional
               _lowerCamelCase	:					Optional[int]   =		torch.tensor(__snake_case   )
         elif (
             key_name.startswith("""model/wte"""   )
             or key_name.startswith("""model/wpe"""   )
             or key_name.startswith("""model/ete"""   )
         ):
            _lowerCamelCase	:					Optional[int]   =		{"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
                key_name[-3:]
            ]
            _lowerCamelCase	:					int   =		"""model.%s.weight""" % nlayer
            _lowerCamelCase	:					Dict   =		vnp.copy()  # same in embedded
            _lowerCamelCase	:					List[Any]   =		torch.tensor(__snake_case   )
            if key_name.startswith("""model/wte"""   ):
               _lowerCamelCase	:					List[Any]   =		"""lm_head.weight"""
               _lowerCamelCase	:					str   =		vnp.copy()  # same in embedded
               _lowerCamelCase	:					List[Any]   =		torch.tensor(__snake_case   )
         elif key_name.startswith("""model/wob"""   ):
            _lowerCamelCase	:					Tuple   =		"""final_logits_bias"""
            _lowerCamelCase	:					List[str]   =		vnp.copy()  # same in embedded
            _lowerCamelCase	:					Optional[int]   =		state.reshape((1, -1)   )
            _lowerCamelCase	:					str   =		torch.tensor(__snake_case   )
         elif key_name == "model/dense/kernel":
            _lowerCamelCase	:					List[str]   =		"""model.last_project.weight"""
            _lowerCamelCase	:					Tuple   =		vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
            _lowerCamelCase	:					Dict   =		torch.tensor(__snake_case   )
         elif key_name == "model/dense_1/bias":
            _lowerCamelCase	:					str   =		"""model.last_project.bias"""
            _lowerCamelCase	:					Optional[int]   =		vnp.copy()  # same because it is one dimensional
            _lowerCamelCase	:					Optional[Any]   =		torch.tensor(__snake_case   )
   torch.save(__snake_case						, args.output   )


if __name__ == "__main__":
 UpperCAmelCase				=						argparse.ArgumentParser(
     description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
 )
 parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
 parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
 UpperCAmelCase				=						parser.parse_args()
 convert_tf_gptsan_to_pt(args)





"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_							=							logging.get_logger(__name__)

lowercase_							=							{
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class    __a						( __snake_case							):
				lowerCamelCase     :       List[Any]			       ='mobilenet_v2'







				def __init__( self      ,	UpperCAmelCase=3      ,	UpperCAmelCase=224      ,	UpperCAmelCase=1.0      ,	UpperCAmelCase=8      ,	UpperCAmelCase=8      ,	UpperCAmelCase=6      ,	UpperCAmelCase=32      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase="relu6"      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0.8      ,	UpperCAmelCase=0.0_2      ,	UpperCAmelCase=0.0_0_1      ,	UpperCAmelCase=255      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					super().__init__(**UpperCAmelCase   )

					if depth_multiplier <= 0:
						raise ValueError('''depth_multiplier must be greater than zero.'''   )

					lowerCAmelCase_      							=			num_channels
					lowerCAmelCase_      							=			image_size
					lowerCAmelCase_      							=			depth_multiplier
					lowerCAmelCase_      							=			depth_divisible_by
					lowerCAmelCase_      							=			min_depth
					lowerCAmelCase_      							=			expand_ratio
					lowerCAmelCase_      							=			output_stride
					lowerCAmelCase_      							=			first_layer_is_expansion
					lowerCAmelCase_      							=			finegrained_output
					lowerCAmelCase_      							=			hidden_act
					lowerCAmelCase_      							=			tf_padding
					lowerCAmelCase_      							=			classifier_dropout_prob
					lowerCAmelCase_      							=			initializer_range
					lowerCAmelCase_      							=			layer_norm_eps
					lowerCAmelCase_      							=			semantic_loss_ignore_index







class    __a						( __snake_case							):
				lowerCamelCase     :       Optional[int]			       =version.parse('1.11'							)







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return OrderedDict([('''pixel_values''', {0: '''batch'''})]   )







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.task == "image-classification":
						return OrderedDict([('''logits''', {0: '''batch'''})]   )
					else:
						return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]   )







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return 1E-4